What would they think?: a computational model of attitudes

ABSTRACT

A key to improving at any task is frequent feedback from people whose opinions we care about: our family, friends, mentors, and the experts. However, such input is not usually available from the right people at the time it is needed most, and attaining a deep understanding of someone else's perspective requires immense effort. This paper introduces a technological solution.We present a novel method for automatically modeling a person's attitudes and opinions, and a proactive interface called "What Would They Think?" which offers the just-in-time perspectives of people whose opinions we care about, based on whatever the user happens to be reading or writing. In the application, each person is represented by a "digital persona," generated from an automated analysis of personal texts (e.g. weblogs and papers written by the person being modeled) using natural language processing and commonsense-based textual-affect sensing.In user studies, participants using our application were able to grasp the personalities and opinions of a panel of strangers more quickly and deeply than with either of two baseline methods. We discuss the theoretical and pragmatic implications of this research to intelligent user interfaces.

REFERENCES

• 1: Bandura, A. (1977). Social Learning Theory. New York: General Learning Press.
• 2: Clynes, M. (1977). Sentics: The Touch of Emotions. Garden City: Anchor Press.
• 3: Deerwester, S. et al. (1990). Indexing by latent semantic anlysis. Journal of Am. Soc. of Info. Sci.:416(6), pp 391--407.
• 4: Freud, S. (1991). The essentials of psycho-analysis by Anna Freud. London: Penguin.
• 5: Jonathan Gratch , Stacy Marsella, Tears and fears: modeling emotions and emotional behaviors in synthetic agents, Proceedings of the fifth international conference on Autonomous agents, p.278-285, May 2001, Montreal, Quebec, Canada [doi>10.1145/375735.376309]
• 6: Liu, H. (2003b). A Computational Model of Human Affective Memory. MIT Media Lab Technical Report. At: web.media.mit.edu/~hugo/publications/papers/ham-tr.doc
• 7: Hugo Liu , Henry Lieberman , Ted Selker, A model of textual affect sensing using real-world knowledge, Proceedings of the 8th international conference on Intelligent user interfaces, January 12-15, 2003, Miami, Florida, USA [doi>10.1145/604045.604067]
• 8: Hugo Liu , Ted Selker , Henry Lieberman, Visualizing the affective structure of a text document, CHI '03 extended abstracts on Human factors in computing systems, April 05-10, 2003, Ft. Lauderdale, Florida, USA [doi>10.1145/765891.765961]
• 9: McCloud, S. (1993). Understanding Comics, Kitchen Sink Press, Northhampton, Maine.
• 10: Mehrabian, A. (1995b). Framework for a comprehensive description and measurement of emotional states. Genetic, Social, and General Psychology Monographs, 121, 339--361.
• 11: Minsky, M., (forthcoming). The Emotion Machine, Pantheon, New York. Several chapters are available at: http://web.media.mit.edu/~minsky.
• 12: Clifford Nass , Jonathan Steuer , Ellen R. Tauber, Computers are social actors, Proceedings of the SIGCHI conference on Human factors in computing systems: celebrating interdependence, p.72-78, April 24-28, 1994, Boston, Massachusetts, United States [doi>10.1145/191666.191703]
• 13: Oren, T., et al. (1990). Guides: characterizing the interface. In Laurel, B. (Eds.) The art of human-computer interface design. Addison-Wesley.
• 14: Rhodes, B. and Starner, T. (1996). The Remembrance Agent: A continuously running automated information retrieval system. Proceedings of PAAM '96, pp. 487--495.
• 15: Upendra Shardanand , Pattie Maes, Social information filtering: algorithms for automating “word of mouth”, Proceedings of the SIGCHI conference on Human factors in computing systems, p.210-217, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223931]
• 16: Singh, P., (2002). The public acquisition of commonsense knowledge. Proceedings of AAAI 2002 Spring Symposium.
• 17: Sison, R. and Shimura, M. (1998). Student modeling and machine learning. International Journal of Artificial Intelligence in Education, 9:128--158.
• 18: Tulving, E. (1983). Elements of episodic memory. Oxford: New York.
• 19: Vemuri, S., et al. (2004). The Design of an Audio-Based Personal Memory Aid. Submitted to CHI '2004.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)
          Subjects: Interaction styles (e.g., commands, menus, forms, direct manipulation)

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)
          Subjects: Natural language

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Algorithms, Design, Human Factors, Languages, Theory

Keywords:
affective interfaces, affective memory, user modeling